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Process Parameter Optimization for Hybrid Manufacturing of PLA Components with Improved Surface Quality.

Sergiu Pascu1, Nicolae Balc1

  • 1Department of Manufacturing Engineering, Faculty of Industrial Engineering, Robotics and Production Management, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania.

Polymers
|September 9, 2023
PubMed
Summary

This study introduces a new hybrid manufacturing method for Polylactic Acid (PLA) components, optimizing 3D printing and milling parameters. Neural network models significantly improve surface roughness prediction for enhanced component quality.

Keywords:
PLA componentsbiodegradable thermoplastic polymerhybrid manufacturingneural network modelingprocess parameters optimizationroughness prediction

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Additive Manufacturing

Background:

  • 3D printing of Polylactic Acid (PLA) components requires process optimization for accuracy and surface quality.
  • Hybrid Manufacturing Equipment (HME) offers potential for producing complex parts with both additive and subtractive techniques.
  • Biodegradable thermoplastic polymers like PLA are increasingly used, necessitating sustainable manufacturing methods.

Purpose of the Study:

  • To present a novel method for optimizing process parameters in the 3D printing of PLA components using Hybrid Manufacturing Equipment (HME).
  • To develop and validate mathematical models (linear regression and neural networks) for predicting and improving surface roughness.
  • To demonstrate the capability of the new HME and the proposed optimization methodology for complex polymer part production.

Main Methods:

  • Development of a new Hybrid Manufacturing Equipment (HME) integrating additive (3D printing) and subtractive (milling) processes.
  • Application of Design of Experiments (DOE) to analyze key manufacturing parameters: spindle rotation, cutting depth, feed rate, layer thickness, nozzle speed.
  • Development of Linear Regression Models (LNM) and Neural Network Models (NNM) to predict surface roughness (Ra).

Main Results:

  • Neural Network Modeling (NNM) demonstrated superior precision in predicting surface roughness compared to Linear Regression Models (LNM).
  • Optimized process parameters were identified for both 3D printing and milling stages of PLA components.
  • A new test part was successfully manufactured using the HME to validate the developed mathematical models and methodology.

Conclusions:

  • The proposed methodology provides an effective approach to optimize process parameters for hybrid manufacturing of polymer materials like PLA.
  • The developed NNM offers a highly accurate tool for predicting surface roughness, enabling enhanced quality control.
  • This research offers a replicable guideline for optimizing process parameters in newly developed hybrid manufacturing equipment, promoting wider equipment functionality.